


import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
			import numpy as np
			import torch

			from . import (
			    AlbertForPreTraining,
			    BartForConditionalGeneration,
			    BertForPreTraining,
			    BertForQuestionAnswering,
			    BertForSequenceClassification,
			    CamembertForMaskedLM,
			    CTRLLMHeadModel,
			    DistilBertForMaskedLM,
			    DistilBertForQuestionAnswering,
			    DPRContextEncoder,
			    DPRQuestionEncoder,
			    DPRReader,
			    ElectraForPreTraining,
			    FlaubertWithLMHeadModel,
			    GPTaLMHeadModel,
			    LayoutLMForMaskedLM,
			    LxmertForPreTraining,
			    LxmertVisualFeatureEncoder,
			    OpenAIGPTLMHeadModel,
			    RobertaForMaskedLM,
			    RobertaForSequenceClassification,
			    TaForConditionalGeneration,
			    TransfoXLLMHeadModel,
			    XLMRobertaForMaskedLM,
			    XLMWithLMHeadModel,
			    XLNetLMHeadModel,
			)


logging.set_verbosity_info()

_snake_case	:						Dict         =							{
    "bart": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "bert": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-base-cased-finetuned-mrpc": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "dpr": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "gpt2": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlnet": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm-roberta": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "transfo-xl": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "openai-gpt": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "roberta": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "layoutlm": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "roberta-large-mnli": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "camembert": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "flaubert": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert-base-distilled-squad": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert-visual-feature-encoder": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "ctrl": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "albert": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "t5": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "electra": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "wav2vec2": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase=False				,      __lowerCamelCase=True      ):
							if model_type not in MODEL_CLASSES:
														raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()      )}.'      )

							__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Optional[Any]									=  MODEL_CLASSES[model_type]

							# Initialise TF model
							if config_file in aws_config_map:
														__snake_case						:     List[Any]									=  cached_file(__lowerCamelCase				,      __lowerCamelCase				,      force_download=not use_cached_models      )
							__snake_case						:     Tuple									=  config_class.from_json_file(__lowerCamelCase      )
							__snake_case						:     Optional[int]									=  True
							__snake_case						:     Optional[int]									=  True
							print(F'Building TensorFlow model from configuration: {config}'      )
							__snake_case						:     str									=  model_class(__lowerCamelCase      )

							# Load weights from tf checkpoint
							if pytorch_checkpoint_path in aws_config_map.keys():
														__snake_case						:     str									=  cached_file(
														    __lowerCamelCase				,      __lowerCamelCase				,      force_download=not use_cached_models      )
							# Load PyTorch checkpoint in tf2 model:
							__snake_case						:     int									=  load_pytorch_checkpoint_in_tfa_model(__lowerCamelCase				,      __lowerCamelCase      )

							if compare_with_pt_model:
														__snake_case						:     Any									=  tf_model(tf_model.dummy_inputs				,      training=__lowerCamelCase      )  # build the network

														__snake_case						:     Tuple									=  torch.load(__lowerCamelCase				,      map_location="cpu"      )
														__snake_case						:     Tuple									=  pt_model_class.from_pretrained(
														    pretrained_model_name_or_path=__lowerCamelCase				,      config=__lowerCamelCase				,      state_dict=__lowerCamelCase      )

														with torch.no_grad():
																					__snake_case						:     List[Any]									=  pt_model(**pt_model.dummy_inputs      )

														__snake_case						:     Optional[int]									=  pto[0].numpy()
														__snake_case						:     Union[str, Any]									=  tfo[0].numpy()
														__snake_case						:     Dict									=  np.amax(np.abs(np_pt - np_tf      )      )
														print(F'Max absolute difference between models outputs {diff}'      )
														assert diff <= 2e-2, F'Error, model absolute difference is >2e-2: {diff}'

							# Save pytorch-model
							print(F'Save TensorFlow model to {tf_dump_path}'      )
							tf_model.save_weights(__lowerCamelCase				,      save_format="h5"      )







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase=None				,      __lowerCamelCase=None				,      __lowerCamelCase=False				,      __lowerCamelCase=False				,      __lowerCamelCase=False				,      __lowerCamelCase=False				,      ):
							if args_model_type is None:
														__snake_case						:     Any									=  list(MODEL_CLASSES.keys()      )
							else:
														__snake_case						:     Optional[int]									=  [args_model_type]

							for j, model_type in enumerate(__lowerCamelCase				,      start=1      ):
														print("=" * 1_0_0      )
														print(F' Converting model type {j}/{len(__lowerCamelCase      )}: {model_type}'      )
														print("=" * 1_0_0      )
														if model_type not in MODEL_CLASSES:
																					raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()      )}.'      )

														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Any									=  MODEL_CLASSES[model_type]

														if model_shortcut_names_or_path is None:
																					__snake_case						:     Optional[int]									=  list(aws_model_maps.keys()      )
														if config_shortcut_names_or_path is None:
																					__snake_case						:     List[str]									=  model_shortcut_names_or_path

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(__lowerCamelCase				,      __lowerCamelCase      )				,      start=1      ):
																					print("-" * 1_0_0      )
																					if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																												if not only_convert_finetuned_models:
																																			print(F'    Skipping finetuned checkpoint {model_shortcut_name}'      )
																																			continue
																												__snake_case						:     Dict									=  model_shortcut_name
																					elif only_convert_finetuned_models:
																												print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'      )
																												continue
																					print(
																					    F'    Converting checkpoint {i}/{len(__lowerCamelCase      )}: {model_shortcut_name} - model_type {model_type}'      )
																					print("-" * 1_0_0      )

																					if config_shortcut_name in aws_config_map:
																												__snake_case						:     List[str]									=  cached_file(__lowerCamelCase				,      __lowerCamelCase				,      force_download=not use_cached_models      )
																					else:
																												__snake_case						:     List[Any]									=  config_shortcut_name

																					if model_shortcut_name in aws_model_maps:
																												__snake_case						:     str									=  cached_file(__lowerCamelCase				,      __lowerCamelCase				,      force_download=not use_cached_models      )
																					else:
																												__snake_case						:     str									=  model_shortcut_name

																					if os.path.isfile(__lowerCamelCase      ):
																												__snake_case						:     List[Any]									=  "converted_model"

																					convert_pt_checkpoint_to_tf(
																					    model_type=__lowerCamelCase				,      pytorch_checkpoint_path=__lowerCamelCase				,      config_file=__lowerCamelCase				,      tf_dump_path=os.path.join(__lowerCamelCase				,      model_shortcut_name + "-tf_model.h5"      )				,      compare_with_pt_model=__lowerCamelCase				,      )
																					if remove_cached_files:
																												os.remove(__lowerCamelCase      )
																												os.remove(__lowerCamelCase      )


if __name__ == "__main__":
			_snake_case	:						int         =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
			)
			parser.add_argument(
			    "--model_type",
			    default=None,
			    type=str,
			    help=(
			        f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
			        "convert all the models from AWS."
			    ),
			)
			parser.add_argument(
			    "--pytorch_checkpoint_path",
			    default=None,
			    type=str,
			    help=(
			        "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
			        "If not given, will download and convert all the checkpoints from AWS."
			    ),
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    help=(
			        "The config json file corresponding to the pre-trained model. \n"
			        "This specifies the model architecture. If not given and "
			        "--pytorch_checkpoint_path is not given or is a shortcut name "
			        "use the configuration associated to the shortcut name on the AWS"
			    ),
			)
			parser.add_argument(
			    "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
			)
			parser.add_argument(
			    "--use_cached_models",
			    action="store_true",
			    help="Use cached models if possible instead of updating to latest checkpoint versions.",
			)
			parser.add_argument(
			    "--remove_cached_files",
			    action="store_true",
			    help="Remove pytorch models after conversion (save memory when converting in batches).",
			)
			parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
			_snake_case	:						str         =							parser.parse_args()

			# if args.pytorch_checkpoint_path is not None:
			#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
			#                                 args.pytorch_checkpoint_path,
			#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
			#                                 args.tf_dump_path,
			#                                 compare_with_pt_model=args.compare_with_pt_model,
			#                                 use_cached_models=args.use_cached_models)
			# else:
			convert_all_pt_checkpoints_to_tf(
			    args.model_type.lower() if args.model_type is not None else None,
			    args.tf_dump_path,
			    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
			    if args.pytorch_checkpoint_path is not None
			    else None,
			    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
			    compare_with_pt_model=args.compare_with_pt_model,
			    use_cached_models=args.use_cached_models,
			    remove_cached_files=args.remove_cached_files,
			    only_convert_finetuned_models=args.only_convert_finetuned_models,
			)






'''simple docstring'''



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      SwinConfig.from_pretrained(
							    '''microsoft/swin-tiny-patch4-window7-224'''       ,		out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      MaskFormerConfig(backbone_config=A					)

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      '''huggingface/label-files'''
							if "ade20k-full" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      847
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''maskformer-ade20k-full-id2label.json'''
							elif "ade" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   List[str]         =      150
														SCREAMING_SNAKE_CASE       :   Any         =      '''ade20k-id2label.json'''
							elif "coco-stuff" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      171
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''maskformer-coco-stuff-id2label.json'''
							elif "coco" in model_name:
														# TODO
														SCREAMING_SNAKE_CASE       :   str         =      133
														SCREAMING_SNAKE_CASE       :   Any         =      '''coco-panoptic-id2label.json'''
							elif "cityscapes" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      19
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''cityscapes-id2label.json'''
							elif "vistas" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Tuple         =      65
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''mapillary-vistas-id2label.json'''

							SCREAMING_SNAKE_CASE       :   List[str]         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {int(A					): v for k, v in idalabel.items()}

							return config
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      []
							# stem
							# fmt: off
							rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')					)
							rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')					)
							rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')					)
							rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')					)
							# stages
							for i in range(len(config.backbone_config.depths					)					):
														for j in range(config.backbone_config.depths[i]					):
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

														if i < 3:
																					rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
														rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
														rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

							# FPN
							rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')					)
							for source_index, target_index in zip(range(3       ,		0       ,		-1					)       ,		range(0       ,		3					)					):
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
							rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')					)

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers					):
														# self-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
														# cross-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
														# MLP 1
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
														# MLP 2
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
														# layernorm 3 (final layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')					)

							# heads on top
							rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')					)

							rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')					)

							rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')					)

							for i in range(3					):
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")					)
							# fmt: on

							return rename_keys
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      dct.pop(A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      val
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[Any]					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
							for i in range(len(backbone_config.depths					)					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      num_features[i]
														for j in range(backbone_config.depths[i]					):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
																					SCREAMING_SNAKE_CASE       :   Any         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
																					# next, add query, keys and values (in that order) to the state dict
																					SCREAMING_SNAKE_CASE       :   int         =      in_proj_weight[:dim, :]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      in_proj_bias[: dim]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[
																					    dim : dim * 2, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[
																					    dim : dim * 2
																					]
																					SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[
																					    -dim :, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[-dim :]
																					# fmt: on
def        UpperCAmelCase (    A	:					Any       ,		A	:					Optional[Any]					):
							# fmt: off
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers					):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   int         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Dict         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
							# fmt: on
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   Tuple         =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE       :   Tuple         =      Image.open(requests.get(A       ,		stream=A					).raw					)
							return im
@torch.no_grad()
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					str       ,		A	:					bool = False					):
							SCREAMING_SNAKE_CASE       :   int         =      get_maskformer_config(A					)

							# load original state_dict
							with open(A       ,		'''rb'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      pickle.load(A					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      data['''model''']

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      create_rename_keys(A					)
							for src, dest in rename_keys:
														rename_key(A       ,		A       ,		A					)
							read_in_swin_q_k_v(A       ,		config.backbone_config					)
							read_in_decoder_q_k_v(A       ,		A					)

							# update to torch tensors
							for key, value in state_dict.items():
														SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.from_numpy(A					)

							# load 🤗 model
							SCREAMING_SNAKE_CASE       :   Any         =      MaskFormerForInstanceSegmentation(A					)
							model.eval()

							for name, param in model.named_parameters():
														print(A       ,		param.shape					)

							SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model.load_state_dict(A       ,		strict=A					)
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(A					) == 0, F"""Unexpected keys: {unexpected_keys}"""

							# verify results
							SCREAMING_SNAKE_CASE       :   Tuple         =      prepare_img()
							if "vistas" in model_name:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      65
							elif "cityscapes" in model_name:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      65535
							else:
														SCREAMING_SNAKE_CASE       :   List[str]         =      255
							SCREAMING_SNAKE_CASE       :   Dict         =      True if '''ade''' in model_name else False
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      MaskFormerImageProcessor(ignore_index=A       ,		reduce_labels=A					)

							SCREAMING_SNAKE_CASE       :   List[str]         =      image_processor(A       ,		return_tensors='''pt'''					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model(**A					)

							print('''Logits:'''       ,		outputs.class_queries_logits[0, :3, :3]					)

							if model_name == "maskformer-swin-tiny-ade":
														SCREAMING_SNAKE_CASE       :   Any         =      torch.tensor(
														    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,		A       ,		atol=1e-4					)
							print('''Looks ok!'''					)

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
														Path(A					).mkdir(exist_ok=A					)
														model.save_pretrained(A					)
														image_processor.save_pretrained(A					)

							if push_to_hub:
														print('''Pushing model and image processor to the hub...'''					)
														model.push_to_hub(F"""nielsr/{model_name}"""					)
														image_processor.push_to_hub(F"""nielsr/{model_name}"""					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='maskformer-swin-tiny-ade',
							    type=str,
							    help=('Name of the MaskFormer model you\'d like to convert',),
							)
							parser.add_argument(
							    '--checkpoint_path',
							    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
							    type=str,
							    help='Path to the original state dict (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							lowerCAmelCase_  :							List[Any]             =    parser.parse_args()
							convert_maskformer_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)




